Prognosis method for blood disorders

ABSTRACT

A method for determining the risk of developing a blood disorder, which includes the steps of: exposing a biological sample from an individual to radiation to obtain a spectrum characteristic of the sample, the spectrum being processed in order to obtain a spectral signature; comparing the spectral signature obtained with one or more reference spectral signatures; and concluding, if the spectral signature of the individual is significantly different from control spectral signatures, that the individual is likely to develop a blood disorder, and, if not, that the individual is not likely to develop a blood disorder.

The invention relates to a prognosis method for blood disorders.

The field of blood diseases generally requires early screening,diagnosis, and tracking of the development of the disease towards a moreserious state, and involves an analysis which can be carried out on aspecific biological sample, which generally adds up to a plurality ofblood tests.

The myelodysplastic syndromes (MDS) are pre-leukemic states, thefrequency of which increases with the aging of the population, and whichexhibits a progressive development with transformation into secondaryacute leukemia in 30% of cases. Their diagnosis requires sampling ofbone marrow in order to perform a cytological analysis by a biologistspecialized in hematology (myelogram).

Such analyses are invasive, long and costly, and it is important to beable to simplify the analyses and to obtain more rapid results.

The Fourier transform infrared (FTIR) spectroscopy is known and used foridentifying organic compounds and examining the biochemical compositionof a biological sample (tissue or fluid).

Processes such as leukemogenesis can cause overall changes in the cellbiochemistry, leading to differences in the absorption spectra when theyare analyzed by FTIR spectroscopy techniques. Consequently, the FTIRspectroscopy is commonly used to distinguish between a normal tissue andan abnormal tissue, by analyzing changes in the absorption bands ofmacromolecules such as fats, proteins, carbohydrates and nucleic acids.

Furthermore, the teaching of the application WO2011121588 is known fromthe prior art, which describes a method and a system for detecting andmonitoring a blood cancer. More particularly, the inventors named inthis application have identified that the samples of mononucleate cellsobtained from leukemia patients produce FTIR spectra which differ fromthose of healthy controls and non-cancer patients suffering clinicalsymptoms which are similar to leukemia, for example subjects sufferingfrom a fever, thus allowing a differential diagnosis of leukemiapatients. By distinguishing the leukemia patients, the patientsexhibiting clinical symptoms similar to leukemia, and healthy controlsubjects, the IR spectroscopy provides an effective diagnosis tool fordiagnosing leukemia and/or other types of hematological malignancies.

However, although this document shows the use of FTIR spectroscopy inthe context of leukemia diagnosis, this document does not provide anytools allowing to refine the diagnosis according to the subtype ofpathologies, or to predict a development of the disease.

There is thus a need to provide a method which makes it possible torefine the detection of the subtype of leukemias, and in particularmyeloid leukemias.

The invention aims to overcome these failings of the prior art.

One of the aims of the invention is to provide a prognosis method whichmakes it possible to determine, in a simple manner, the prognosis ofonset of a blood disorder in an individual.

The invention thus relates to a method for determining, in particular invitro, the risk for an individual of developing a blood disorder, from abiological sample of said individual, said method comprising thefollowing steps:

-   -   exposing said biological sample to mid-infrared radiation (MIR)        of wavelength varying from 4000 cm⁻¹ to 400 cm⁻¹ to obtain a        spectrum characteristic of said sample, said spectrum being        processed in order to obtain a spectral signature made up of        absorbance peaks characteristic due to their position, or wave        number, and their intensity, or absorbance, of the type and the        relative concentrations of the various molecules present in said        sample;    -   comparing said spectral signature obtained in the previous step        with one or more reference spectral signatures, said one or more        reference spectral signatures being obtained from a reference        population of individuals; and    -   concluding        -   if the intensities of a first group of peaks of the spectral            signature of said individual is significantly different from            the intensities of these same peaks obtained in the            reference spectral signature(s), that the individual is            likely to develop a blood disorder,        -   the first group of peaks corresponding to the wave numbers            of the following first group: 1330 cm⁻¹, 1445 cm⁻¹, 1478            cm⁻¹, 1493 cm⁻¹, 1505 cm⁻¹, 1507 cm⁻¹, 1520 cm⁻¹, 1526 cm⁻¹,            1544 cm⁻¹, 1571 cm⁻¹, 1602 cm⁻¹, 1668 cm⁻¹, 1674 cm⁻¹, 1676            cm⁻¹, 1697 cm⁻¹, and 2852 cm⁻¹,        -   if not, that the individual is not likely to develop a blood            disorder.

The invention is based on the surprising finding, made by the inventors,that the determination of an infrared spectrum of simple blood samplesobtained from individuals makes it possible to obtain informationrelating to the risk of said individual developing a blood disorder.

Spectroscopy is a simple and quick method which does not require anyreagents for implementation (other than the suitable material), whichmakes it possible to obtain information relating to the macromolecularstructure of compounds contained in a biological sample. Typically, theinfrared spectra (FTIR) are made up of a plurality of absorption bands,each corresponding to specific functional groups associated with cellcomponents such as fats, proteins, carbohydrates and nucleic acids. Allphysiological modifications occurring in an individual, includingcarcinogenesis, can lead to overall changes in the metabolism, whichwill change the absorption spectra when the sample is analyzed by FTIRtechniques. Consequently, the FTIR is commonly used to distinguishbetween a normal tissue and an abnormal tissue, by analyzing changes inthe absorption bands of the molecules.

The infrared portion of the electromagnetic spectrum is divided intothree regions: near, mid and far infrared, designated with respect tothe visible spectrum. Far infrared, extending approximately from 400 to10 cm⁻¹ (1000-25 μm, in practice, spectrum 1000-30 μm), adjoining themicrowave region, has a low energy and can be used for rotationalspectroscopy. Mid infrared radiation, extending approximately from 4000to 400 cm−1 (25-2.5 μm, in practice, spectrum 30-1.4 μm), can be used tostudy the fundamental vibrations of the associated vibrationalstructure. Near infrared, which is higher energy, extendingapproximately from 14000 to 4000 cm⁻¹ (2.5-0.7 μm, in practice, spectrum1.4-0.8 μm), can excite the harmonic vibrations. The designations andclassifications of these subregions are essentially conventions. In theinvention, reference will be made to spectroscopy in the mid infraredrange, according to the definition above.

The infrared spectrum of a sample is established by passing an infraredlight beam through said sample. Studying the transmitted light indicatesthe amount of energy absorbed at each wavelength. This can be achievedusing a monochromatic beam, with modification of the wavelength overtime, or by using a Fourier transform instrument in order to measure allthe absorbances simultaneously, by means of interferometry. It is thuspossible to produce absorbance or transmittance spectra, and analyze theabsorption wavelengths. The analysis of these features reflects themolecular structures of the sample.

This technique functions almost exclusively on the samples havingcovalent bonds. Simple spectra are obtained from samples having fewactive bonds in the infrared range, and at high levels of purity. Morecomplex molecular structures lead to more absorption bands, and thus tomore complex spectra, but this method is still used for characterizingvery complex mixtures.

The method described in the present invention resides in the simplicityof the steps implemented:

-   -   a first step consists in obtaining an infrared spectrum from a        biological sample of an individual, and    -   the second step consists in comparing the spectrum obtained in        the preceding step with one or more reference spectra, in order        to conclude the risk, or otherwise, of developing a blood        disorder.

In the invention, “blood disorder” means any pathology affecting thecomponents of the blood, and in particular the malignant blood disorderssuch as leukemia, lymphomas, myelomas, as well as myelodysplastic andmyeloproliferative syndromes

In the invention, the result of the exposure to the infrared radiationwill be processed, in particular by Fourier transform, in order toobtain a spectral signature that is characteristic of a given sample.

More particularly, the focus will be on the following specific wavenumbers (inverse of the wavelength): 1330 cm⁻¹, 1445 cm⁻¹, 1478 cm⁻¹,1493 cm⁻¹, 1505 cm⁻¹, 1507 cm⁻¹, 1520 cm⁻¹, 1526 cm⁻¹, 1544 cm⁻¹, 1571cm⁻¹, 1602 cm⁻¹, 1668 cm⁻¹, 1674 cm⁻¹, 1676 cm⁻¹, 1697 cm⁻¹, and 2852cm⁻¹, as well as the relative intensity of each of the peakscorresponding to said wave numbers following the Fourier transformation.

Thus, to summarize, a given sample is exposed to mid infrared radiationin order to obtain a spectrum which will be processed by Fouriertransform in order to obtain a spectral signature for at least the wavenumbers 1330 cm⁻¹, 1445 cm⁻¹, 1478 cm⁻¹, 1493 cm⁻¹, 1505 cm⁻¹, 1507cm⁻¹, 1520 cm⁻¹, 1526 cm⁻¹, 1544 cm⁻¹, 1571 cm⁻¹, 1602 cm⁻¹, 1668 cm⁻¹,1674 cm⁻¹, 1676 cm⁻¹, 1697 cm⁻¹, and 2852 cm⁻¹.

Once said spectral signature is obtained, it is compared with areference spectral signature, or a plurality of reference spectralsignatures.

Said reference spectral signatures are obtained from reference samplessubjected to the same infrared treatment (and Fourier transform) as thesample analyzed. In order for the comparison to be more effective, it isessential that the reference spectral signatures should be obtained frombiological samples of the same type (for example blood, serum, plasma,etc.) as the biological sample tested. Thus, by way of example, if ablood sample is tested for an individual, according to the method of theinvention, the reference sample(s) will be those obtained from otherblood samples.

The reference spectral signatures are obtained from referenceindividuals who may be either healthy individuals, i.e. individuals whodo not have any disease, or individuals who have an illness of which thesymptoms are different from those of a blood disorder, as defined in theinvention.

A reference individual can also correspond to the individual testedaccording to the method of the invention, the reference samples havingbeen taken before said individual developed, or was likely to develop, ablood disorder.

During the comparison between the spectral signature of the individualwith the reference spectral signatures, the intensity (absorbance) ofvarious peaks corresponding to the above-mentioned wave numbers iscompared.

In this comparison, it follows that, if all the intensities of the peakscorresponding to said wave numbers are significantly different (increaseor decrease) with respect to the intensity of these same peaks in thereference spectral signatures, then said individual whose sample hasbeen tested according to the method of the invention will be likely todevelop a blood disorder. The signature of a given individual is thusmade up of a pattern-type structure which is distributed over a set ofspectral variables (16 in the present case). A reference signature(healthy or pathological) is thus formed by a “profile”, the pattern.For each type of patient, a profile is identified which is specific ofthe physiological state of the individual. The identification of thephysiological state of any individual is thus based on the comparison ofthis profile (or pattern) with respect to the reference profiles (orpatterns). A calculation of distance between the pattern of theindividual and the reference pattern(s) makes it possible to allocatesaid individual to a particular class/category (healthy or ill, forexample). The individual will be “classified” according to the referencepattern which is at the shortest distance/the closest in a space of (inthis case for example) 16 dimensions.

Thus, on the basis of an FTIR spectrum obtained from a biological sampleof blood, or of a sub-product of blood, or of bone marrow, it ispossible to determine the risk, in an individual, of developing amalignant blood disorder.

Advantageously, the invention relates to the above-mentioned method,wherein, when the individual is likely to develop a blood disorder, itis furthermore concluded that:

-   -   if the intensities of a second group of peaks of the spectral        signature of said individual is significantly different from the        intensities of these same peaks obtained in the reference        spectral signature(s), that the individual is likely to develop        leukemia,

the second group of peaks corresponding to the wave numbers of thefollowing first group: 3316 cm⁻¹, 3283 cm⁻¹, 3281 cm⁻¹, 3256 cm⁻¹, 3118cm⁻¹, 3116 cm⁻¹, 1345 cm⁻¹, 1343 cm⁻¹, 1340 cm⁻¹ and 1338 cm⁻¹,

-   -   if not, that the individual is likely to develop a        myelodysplastic syndrome.

The inventors have demonstrated that, if the first group of peaks, orwave number, makes it possible to determine if an individual is likelyto develop a blood disorder, it is possible, by studying a second groupof peaks of the spectral signature, and according to the differencesobtained, to determine if the individual in question, tested accordingto the method of the invention, is likely to develop leukemia or amyelodysplastic syndrome.

The myelodysplastic syndromes (MDS) are clonal blood disorders acquiredfrom the medullary hematopoietic stem cells, with excessiveproliferation of myeloid progenitors which are differentiated in anabnormal manner (=dysmyelopoiesis). The excessive apoptosis of theprecursors results in a production failure and in peripheral cytopenia(=ineffective hematopoiesis).

There are a plurality of classes of these, defined by the WHO (2016)depending on the type and the number of cytopenia, signs ofmyelodysplasia (morphological abnormalities of the medullary cells), thepresence or absence of an excess of blasts.

The development is extended and relatively indolent in 70% of cases,with progressive aggravation of the cytopenia (bone marrow failure). In30% of cases, the development is quicker and more aggressive towards anacute myeloid leukemia by accumulation of blasts, explaining why the MDSare also referred to as “pre-leukemic states”.

According to this embodiment, studying the two first groups of wavenumbers of the spectral signature does not make it possible todistinguish MDS said to be “low-risk” from MDS said to be “high-risk”for transition towards secondary leukemia.

In the invention, a distinction is in particular made between two typesof leukemia, in particular two types of acute myeloid leukemia—de novoacute myeloid leukemia, and secondary myeloid leukemia.

The de novo leukemias occur spontaneously in patients, and directly,without being detected in the patient prior to the myeloproliferativesyndrome. Such leukemias may appear on account of the simultaneousaccumulation of abnormalities affecting the cell proliferation anddifferentiation of myeloid progenitors.

In turn, the secondary acute myeloid leukemias occur following aworsening of a myeloproliferative syndrome, in particular byaccumulating genetic abnormalities which inhibit the differentiation ofthe progenitors.

In an advantageous embodiment, the invention relates to theabove-mentioned method, wherein, when the individual is likely todevelop a myelodysplastic syndrome, it is concluded that:

-   -   if the intensities of a third group of peaks of the spectral        signature of said individual is significantly different from the        intensities of these same peaks obtained in the reference        spectral signature(s), that the individual is likely to develop        a low-risk myelodysplastic syndrome,

the third group of peaks corresponding to the wave numbers of thefollowing first group: 3060 cm⁻¹, 3062 cm⁻¹, 3396 cm⁻¹, 3384 cm⁻¹ and3052 cm⁻¹,

-   -   if not, that the individual is likely to develop a high-risk        myelodysplastic syndrome.

By means of the first, second and third groups of peaks of the spectralsignatures, it is possible to distinguish the occurrence of a low-riskor high-risk myelodysplastic syndrome.

Advantageously, the invention relates to the method described above,wherein, when the individual is likely to develop leukemia, it isconcluded that:

-   -   if the intensities of a fourth group of peaks of the spectral        signature of said individual is significantly different from the        intensities of these same peaks obtained in the reference        spectral signature(s), that the individual is likely to develop        a secondary leukemia,

the fourth group of peaks corresponding to the wave numbers of thefollowing first group: 3270 cm⁻¹, 3268 cm⁻¹, 3266 cm⁻¹, 3264 cm⁻¹, 3192cm⁻¹, 3190 cm⁻¹, 2850 cm⁻¹, 2840 cm⁻¹, 1707 cm⁻¹, 1705 cm⁻¹, 1664 cm⁻¹,1662 cm⁻¹, 1633 cm⁻¹, 1631 cm⁻¹, 1493 cm⁻¹, 1491 cm⁻¹, 1489 cm⁻¹, 1458cm⁻¹, 1456 cm⁻¹ and 1256 cm⁻¹,

-   -   if not, that the individual will be likely to develop to develop        de novo leukemia.

By means of the first, second and fourth groups of peaks of the spectralsignatures, it is possible to distinguish the occurrence of a de novoleukemia or a secondary leukemia.

Even more advantageously, the invention relates to the above-mentionedmethod, wherein said biological sample is a blood plasma sample.

The advantageous biological sample for implementing the invention isblood plasma which can be obtained during a routine blood test.

The blood plasma is the liquid fraction of the blood. It makes upapproximately 55% of the blood volume and serves to transport the bloodcells, the platelets and the hormones, and other soluble components(proteins, metabolites, hormones, salts, etc.) through the organism.

Advantageously, the invention relates to the above-mentioned method,wherein the spectral signature and the reference spectral signature(s)are obtained via the second derivative of the respective infraredspectroscopy data.

The calculation of the second derivative of each of the spectra isadvantageously carried out. Said second derivative improves theresolution of the infrared bands, as well as the distinction of thepeaks obtained. The second derivation of the infrared spectra provides aclear improvement with respect to the use of raw spectra (non-derived)for the characterization, and the identification, of compounds containedin a sample.

This processing is carried out using a software generally integrated inthe spectrometer.

The invention also relates to the use of a blood plasma sample of anindividual for determining, in particular in vitro, the risk of saidindividual developing a blood disorder, wherein

-   -   if the intensities of peaks corresponding to a first group of        wave numbers of a spectral signature obtained, for said sample,        by infrared spectroscopy, are significantly different from the        intensities of the same peaks obtained from the spectral        signature of one or more control individuals, that the        individual is likely to develop a blood disorder,

said first group of wave numbers corresponding to the following wavenumbers: 1330 cm⁻¹, 1445 cm⁻¹, 1478 cm⁻¹, 1493 cm⁻¹, 1505 cm⁻¹, 1507cm⁻¹, 1520 cm⁻¹, 1526 cm⁻¹, 1544 cm⁻¹, 1571 cm⁻¹, 1602 cm⁻¹, 1668 cm⁻¹,1674 cm⁻¹, 1676 cm⁻¹, 1697 cm⁻¹, and 2852 cm⁻¹,

-   -   if not, that the individual is not likely to develop a blood        disorder.

Advantageously, the invention relates to the above-mentioned use,wherein, if the individual is likely to develop a blood disorder, and

-   -   if the intensities of peaks corresponding to a second group of        wave numbers of a spectral signature obtained, for said sample,        by infrared spectroscopy, are significantly different from the        intensities of the same peaks obtained from the spectral        signature of one or more control individuals, that the        individual is likely to develop leukemia,

said second group of wave numbers corresponding to the following wavenumbers: 3316 cm⁻¹, 3283 cm⁻¹, 3281 cm⁻¹, 3256 cm⁻¹, 3118 cm⁻¹, 3116cm⁻¹, 1345 cm⁻¹, 1343 cm⁻¹, 1340 cm⁻¹ and 1338 cm⁻¹,

-   -   if not, that the individual is likely to develop a        myelodysplastic syndrome.

Even more advantageously, the invention relates to the above-mentioneduse, wherein

-   -   if the individual is likely to develop a myelodysplastic        syndrome,        -   if the intensities of peaks corresponding to a third group            of wave numbers of a spectral signature obtained, for said            sample, by infrared spectroscopy, are significantly            different from the intensities of the same peaks obtained            from the spectral signature of one or more control            individuals, that the individual will be likely to develop a            low-risk myelodysplastic syndrome,        -   said third group of wave numbers corresponding to the            following wave numbers: 3060 cm⁻¹, 3062 cm⁻¹, 3396 cm⁻¹,            3384 cm⁻¹ and 3052 cm⁻¹,            -   if not, that the individual will be likely to develop a                high-risk myelodysplastic syndrome, and            -   if the individual is likely to develop leukemia,            -   if the intensities of peaks corresponding to a fourth                group of wave numbers of a spectral signature obtained,                for said sample, by infrared spectroscopy, are                significantly different from the intensities of the same                peaks obtained from the spectral signature of one or                more control individuals, that the individual will be                likely to develop a secondary leukemia,        -   said fourth group of wave numbers corresponding to the            following wave numbers: 3270 cm⁻¹, 3268 cm⁻¹, 3266 cm⁻¹,            3264 cm⁻¹, 3192 cm⁻¹, 3190 cm⁻¹, 2850 cm⁻¹, 2840 cm⁻¹, 1707            cm⁻¹, 1705 cm⁻¹, 1664 cm⁻¹, 1662 cm⁻¹, 1633 cm⁻¹, 1631 cm⁻¹,            1493 cm⁻¹, 1491 cm⁻¹, 1489 cm⁻¹, 1458 cm⁻¹, 1456 cm⁻¹ and            1256 cm⁻¹,            -   if not, that the individual will be likely to develop to                develop de novo leukemia.

The invention furthermore relates to a computer program product, orsoftware, comprising portions, means or program code instructions forexecuting the steps of the method as defined above, when said program isexecuted on a computer.

Advantageously, said program is included in a computer-readable datarecording medium. A medium of this kind is not limited to a portablerecording medium such as a CD-ROM, but can also be part of a devicecomprising an internal memory, in a computer (for example RAM and/orROM), or of an external memory device such as hard disks or USB keys, ora nearby or remote server.

Advantageously, the above-mentioned computer program product or softwareis designed to allow for

-   -   the processing of infrared spectra in order to obtain the        spectral signature,    -   the comparison of the spectral signature obtained from the        biological sample of the individual tested with the reference        spectral signatures,    -   or both.

The above-mentioned computer program product or software can alsoadvantageously be used for forming a second derivative of the spectraobtained after the Fourier transform.

The invention will be better understood in the light of the figure andthe following examples.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a table showing the synthetic results of the intergroupdistinction G0, G1, G2, G3 and G4. G0: Healthy; G1: Low-riskmyelodysplastic syndromes; G2: High-risk myelodysplastic syndromes; G3:Secondary acute myeloid leukemia and G4: De novo acute myeloid leukemia.

EXAMPLES Example 1: Classification

Material and Methods

A—Biological Samples

The blood plasmas were isolated by double centrifugation (700 g, 10 min)from whole blood of unwell individuals (n=70) included in the studyMYLESYM (ID-RCE 2011-A00271-40) who had given their consent. They werecompared with the plasmas of 99 healthy donors recruited within thecontext of the study HEALTHOX (ClinicalTrials.gov #NCT02789839).

The plasma samples (50 μL) are frozen at −80° C. until being used. Oncedefrosted at ambient temperature and homogenized using a vortex-typeagitator, 5 μL are deposited and spread on a multi-well plate, of 96positions, made of silica or zinc selenide (ZnSe), materials which aretransparent in IR, set to dry for 15 minutes in a sterilizer at 35° C.,and analyzed using an MIR spectrophotometer.

Alternatively, the samples (20 μL) can be deposited on a microscopeslide and left in the open air for drying for 24 hours.

B—Acquisition of Blood Plasma Samples

Samples n=169 (99 healthy/70 unwell controls)

G0: Healthy (60 women, 39 men),

G1: Low-risk myelodysplastic syndromes: LR-MDS (12 women, 26 men),

G2: High-risk myelodysplastic syndromes: HR-MDS (5 women, 4 men),

G3: Secondary acute myeloid leukemia AML Sec (3 women, 8 men),

G4: De novo acute myeloid leukemia: AML-Novo (9 women, 7 men).

C—LUMOS Microscope (Bruker)

The LUMOS is an autonomous IRTF microscope equipped with an integratedspectrometer. The innovation provided by a motorized crystal allows thesystem to pass from the Transmission mode to the Reflection and ATR modewithout any intervention by the operator, and to measure, in an entirelyautomated manner, a sample or a background noise, even when the ATR modeis activated. This type of apparatus is suitable for the attenuatedtotal reflectance (ATR) measurements if the samples were deposited onglass slides, a material which is not transparent in mid-infrared.

An analogue to the de Bruker IR Biotyper can also be used. Theinstrument is driven by software belonging to de Bruker which is OPUS.This type of spectrophotometer makes it possible to easily acquirearound one hundred spectra per day, including plate preparation. Theinfrared spectra are thus connected in “Transmission” mode, the infraredbeam passing through the sample, and the multi-well plate which is madeof crystalized ZnSe, a material transparent in the mid-infrared range.

In all cases (measurements in reflection or in transmission) thespectral resolution is 4 cm−1 and 64 to 128 scans are averaged. Thebackground noise is measured via an empty well. The “raw” absorptionspectra (as they are) are then saved and then exported to Jcamp format(“open” format) using a macro routine under OPUS.

D—Test Quality

In order to evaluate the quality of spectra on the basis of a pluralityof parameters: The water vapor, the signal/water ratio, the intensity ofthe noise, etc. and to identify the aberrant spectra (outliers) which donot meet certain criteria. In order to verify the hydration state of thesample, it is ensured that the Amide I band of the proteins (1650 cm⁻¹)is 2 or 3 times greater than the band of (3400 cm⁻¹) which essentiallyreflects the liquid water.

E—Baseline Correction

The variation in the baseline may be caused by a change in theconditions during acquisition, or variations associated with theinstrumentation or the environment (for example: temperature).

F—Normalization

With the aim of minimizing the intensity differences of the signal whichare not linked to the sample but to the instrumentation, the raw spectraare normalized using an MSC (Multiplicative Scattering Correction)anti-scattering algorithm: this is a spectral correction method (Sun.D-Wet al. 2009).

G—Filtration

This treatment consists in selecting the spectral range of interestdepending on the sample. A spectral domain of 3800 to 940 cm⁻¹ s fixedon the sample of interest (plasma). On the band 2800 to 1800 cm⁻¹, thespectrum is truncated because it does not contain any information ofinterest for the analyses performed. This contains mainly thecontribution of atmospheric CO₂, which reflects environmentalvariations.

H—Second Derivatives

The derivation makes it possible to improve the resolution of spectra,and thus to limit the effects of band overlap. It will be noted that thepassage from the raw spectrum to the second derivative reduces thesignal-to-noise ratio {Martens H et al. 2002}. The second derivatives ofthe spectra are calculated using 13 points for the sliding windowSavitsky-Golay smoothing.

Analyses of Spectra Data

A—Statistical Methods

1. Non-Supervised Analysis (Descriptive Analysis)

PCA: Principle Component Analysis: This is a first-line analysis, makingit possible to understand the data structure and to identify possiblespectra referred to as outliers, which exhibit a different spectralprofile for technical reasons, for example poor acquisition, or forbiochemical reasons.

2. Supervised Analysis (Explanatory Analysis)

PLSR: Regression by the least squares method (Partial Least SquaresRegression): This is a statistical method which makes it possible tomodel the complex relations between quantitative variables observed,referred to as manifest, and latent variables (MIR spectrum).

B—Selection of Variables

The selection of variables by a genetic algorithm or FADA method makesit possible to identify a sub-set of discriminating variables forspecifying the types of biochemical markers modified by the pathology(Trevisan J et al. 2014). This has two advantages:

-   -   Improving the performance of the model in terms of prediction        (Jouan-Rimbaud D et al. 1995).    -   Improving the interpretation of the models and understanding the        system studied.

C—FADA and GLM Algorithms

An LDA/logistical regression analysis makes it possible to identify themost discriminating spectral variables, in this case between the healthyand different groups of unwell subjects. On the bases of these mostdiscriminating variables, a progressive selection is made in order toidentify the few variables which allow for the best specificities andselectivities.

D—Prediction Principle

The results of the discriminating analysis tests are conventionallyshown in the form of a confusion matrix, to be interpreted as shownbelow in Table 1.

TABLE 1 Measured (diagnostic) Correctly classified Positive Negative (%)Prediction Positive TP FP PPV = TP/(TP + FP) IR Negative FN TP NPV =TN/(TN + FN) Se = TP/ Sp = TN/ (TP + FN) (FP + TN) The numbers in boldrepresent the CORRECTLY classified samples, and underlined, theINCORRECTLY classified ones. The PPV and NPV show the classificationsuccess percentage TP: True positive, TN: True negative, FN: Falsenegative, FP: False positive PPV: the positive predictive value, NPV:the negative predictive value Se: Sensitivity, Sp: Specificity.

The results obtained for this study are shown in the following table 2,and in FIG. 1 .

TABLE 2 Discriminating Correctly variables Discriminated classified TESTn (cm⁻¹) groups Measured Se Sp (%) F1 169 1330, 1445, 1478, predictionG0 3197   6 0.99 0.99 99 1493, 1505, 1507, [G1-G4]  3 2394  99 1520,1526, 1544, 1571, 1602, 1668, 1674, 1676, 1697, 2852 F2 137 1330, 1478,1520, prediction G0 1611  723 0.80 0.79 69 1668, 1697, 2852 G1 389 2877 88 F3 153 1330, 1445, 1493, prediction G0 3242   94 0.98 0.94 97 1505,1507, 1520, [G1-G3]  58 1706  97 1526, 1571, 1666, 1668, 1674 F4 1151054, 1056, 1122, prediction G0 3189   39 0.99 0.93 99 1124, 1493, 1520,G4  11 561 98 1571, 1602, 1666, 1668, 1674 F5 23 3270, 3268, 3266,prediction G3 128 292 0.32 0.70 30 3264, 3192, 3190, G4 272 708 72 2850,2840, 1707, 1705, 1664, 1662, 1633, 1631, 1493, 1491, 1489, 1458, 1456,1256 F6 47 3060, 3062, 3396, prediction G1 1323  239 0.95 0.20 84 3384,3052 G2  68  61 47 F7 16 1705, 1182, 1174, prediction G2 191 186 0.630.38 51 1060, 1058, 1056 G3 109 114 51 F8 146 3339, 3384, 3062,prediction G0 3194  156 0.96 0.90 95 3060, 3052 [G1 + G2] 106 1444  93F9 122 1668, 1666, 1526, prediction G0 3175  238 0.96 0.70 93 1507,1505, 1493 [G3 + G4] 125 562 82 F10 70 3316, 3283, 3281, prediction[G1 + G2] 1376  364 0.86 0.55 79 3256, 3118, 3116, [G3 + G4] 224 463 661345, 1343, 1340, 1338

This study establishes that the myelodysplastic syndromes and the acuteleukemias (de novo or secondary) are accompanied by distinct metabolicchanges which are revealed via the specific IVIIR spectral signatures(specific “barcodes”). This opens up interesting possibilities in termsof early and rapid diagnosis for:

-   -   The identification of molecules of plasma which could reflect        the physiopathology and be significant biomarkers        (interpretation of the spectral signatures);    -   An aid for early detection of myelodysplasias; and    -   An aid for tracking MDS patients.

Example 2

The files imported under OPUS are then imported and transposed in amatrix using a software written in the R environment:

 ################################################################  #ImportJcamp.R  # Directory (Jcamp files and recording of the Excel file) rm(list=ls( )) # deletes all the m√ ©mory  activeRep <−″~/Documents/Rcore/Rdata/Cancer/″  setwd(activeRep)  #Name of Excel fileexport?  excelout <− ″Mad.xlsx″  #Number of attributes provided  N <− 2 ################################################  #Selection of filesof int?r?t  list_files <− list.files(activeRep, pattern=c(″dx″,″JDX″),full.names=TRUE)  #list_files <− list.files(activeRep, full.names=TRUE) # Number of files  n <− length(list_files)  #Library read JDX library(readJDX)  #Wave number on a data.frame  NODF <−readJDX(list_files[1])  NODF <− NODF[[4]]  FCE <− data.frame(NODF$x)  #Addition of empty columns for the attributes  ATTRIBUTES <−data.frame(c(1:N))  for (i in 1:n) {   A <− c(1:N)   ATTRIBUTES <−data.frame(cbind(ATTRIBUTES,A))  }  ATTRIBUTES[c(0:N),c(0:n+1)] <− ″″ #The column is named (on data.frame attributes to simplifier apr?scbind)  names(ATTRIBUTES)[1] <− “Name of the sample″  #Addition of theremainder of the other column (and they are also named on thedata.frame)  for (i in 1:n) {   ACDF <− readJDX(list_files[i])    ACDF<− ACDF[[4]]    FCE <− cbind(FCE,ACDF$y)    names(ATTRIBUTES)[i+1] <−basename(unlist(strsplit(list_files[i],c(″.JDX″,″.dx″))[1]))  } #Inversion lines and columns (=transposition)  FCE <− t(FCE) ATTRIBUTES <− t(ATTRIBUTES)  #All is regrouped  FCE <−cbind(ATTRIBUTES,FCE)  #Export in Excel format  library(openxlsx)  wb1<− createWorkbook( )  sheet <− addWorksheet(wb1,sheetName = “Raw data″) writeData(wb1,sheet = “Raw data″,FCE,colNames = FALSE,rowNames = TRUE) saveWorkbook(wb1, excelout)

At the end of this program, an Excel file is created, the first tab ofwhich contains the transposed matrix (1 sample=one line) of all thesamples to be processed.

The following step consists in calculating the second derivatives ofeach spectrum, smoothing these derivatives by means of the Savitzky andGolay sliding window routine over 11 or 13 points, then truncating thesederivatives in order to keep only the frequency domains relevant for theanalysis. The spectral domains retained are, in almost all cases,3800-2800 cm−1 and 1800-700 cm−1. They are then normalized according tothe vector normalization principle (the area of the second derivative isnormalized to 1). The matrix of the second derivatives is saved in asecond tab of the same Excel file. The following script performs thispre-processing.

 #####################################################  #Pr√ ©processing.R  #Directiry (Jcamp files and recording of the Excelfile)  activeRep <− ″~/Documents/Rcore/Rdata/CANCER″  setwd(activeRep) # Number of attributes  N <− 2  #Truncating (T1>T2>T3>T4)  ″T1″ <− 3600 ″T2″ <− 2800  ″T3″ <− 1800  ″T4″ <− 700  #Size of the sliding smoothingwindow  w<−11  #File name  excelout <− ″Mad.xlsx″ #################################################################################  #Packages  library(RcppArmadillo)  library(prospectr) library(tidyverse)  library(dplyr)  library(ppls)  library(openxlsx) library(hyperSpec)  library(plotrix)  #File import  Import <−read.xlsx(excelout,colNames = FALSE)  write.csv(Import,′Import.csv′)  NF<− read.csv(′Import.csv′)  file.remove(′Import.csv′)  NF <− NF[,−1] #Table of attributes (Number of columns depending on the number ofattributes)  MAT.ATTRIBUTES <− data.frame(NF[,1:(N+1)])  # Data table MAT.ABS <− data.frame(NF[,−c(1:(N+1))])  #Second derivative andsmoothing  P <− (w−1)/2  LMA <− length(MAT.ABS)  MAT.D2 <−rbind(MAT.ABS[1,−c(1:P,(LMA-P+1):LMA)],savitzkyGolay(MAT.ABS[−1,],p=3,w,m=2))  #Normalization of D2  MAT.D2 <−rbind(MAT.D2[1,],normalize.vector(MAT.D2[−1,]))  #Truncating D2N if(T1 > max(MAT.D2[1,])){   ″RT1″ <− 1   }else{   RT1 <−which(MAT.D2[1,]<=T1)   RT1 <− min(RT1)  }  RT2 <− which(MAT.D2[1,]<=T2) RT2 <− min(RT2)  RT3 <− which(MAT.D2[1,]<=T3)  RT3 <− min(RT3)  if(T4 <min(MAT.D2[1,])){   RT4 <− length(MAT.D2[1,])   }else{   RT4 <−which(MAT.D2<=T4)   RT4 <− min(RT4)  }  MAT.D2NT <−cbind(MAT.D2[,c(RT1:RT2,RT3:RT4)])  #Final tables:  #Table Secondderivative, normalized and truncated  D2NT <−cbind(MAT.ATTRIBUTES,MAT.D2NT)  #Display of graphics  pdf(file = ″Plotof pre-processing steps.pdf″)  par(mfrow=c(2,1))  Y1 <− MAT.ABS  Y1 <−Y1[−1,]  Y1 <− as.matrix(Y1)  X1 <− as.vector(as.numeric(MAT.ABS[1,])) RawSpectrum <− new(″hyperSpec″,spc=Y1,wavelength = X1) plotspc(RawSpectrum,wl.reverse = TRUE,wl.range = c(T4 ~ T3,T2 ~T1),xoffset = 990,   title.args = list (xlab = expression(“Wave number ″(cm{circumflex over ( )}−1)),main=”Raw data″))  Y2 <− MAT.D2NT  Y2 <−Y2[−1,]  Y2 <− as.matrix(Y2)  X2 <− as.vector(as.numeric(MAT.D2NT[1,])) D2Spectrum <− new(″hyperSpec″,spc=Y2,wavelength = X2) plotspc(D2Spectrum,wl.range = c(T4 ~ T3,T2 ~ T1),xoffset =990,wl.reverse = TRUE,   title.args = list (xlab = expression(“Wavenumber ″ (cm{circumflex over ( )}−1)),main=”Normalized truncatedderivatives″))  #Export of plot e=in PNG  dev.off( )  #Export in Excelformat  wb <− createWorkbook( )  RD <− “RawData″ # raw data tab  secD <−″D2Norm″ # normalized second derivatives tab  sheet <−addWorksheet(wb,sheetName = RD)  sheet2 <− addWorksheet(wb,sheetName =secD)  writeData(wb,sheet = RD,NF,colNames = F,rowNames = F) writeData(wb,sheet = secD,D2NT,colNames = F,rowNames = F) saveWorkbook(wb,excelout,overwrite = T)  #Export D2NT in format .csvfor PCA  colnames(D2NT) <− D2NT[1,]  write.csv(D2NT,″D2NT.csv″)  NotaBene: these steps can be performed using any kind of calculationsoftware, as the mathematical operations are standard. However, it isimportant to respect the order in which they are performed.

Some authors prefer to work from raw spectra corrected for scattering(Multiple Scattering Correction or MSC routine). The inventors foundbetter performances when working from second derivatives.

These second derivatives, truncated and normalized, are used for thecalibration of predictive models.

The predictive model is based on an analysis of the PLSR type (PartialLeast Squares Regression) which makes it possible to identify the mostdiscriminatory spectral variables between the two groups. Thesevariables are ordered according to the number of times they werepositively selected over a large number of iterations (usually 100).Manual tests are then carried out in order to reduce, as best aspossible, the variables which will have to be taken into account in thepredictive model. Each time (for each combination of variables) aconfusion matrix is calculated which makes it possible to identify thesamples correctly and incorrectly classified.

Once this optimization has been performed, a validation is carried outby predicting samples which did not serve for calibration of thepredictive model. The script R, below, makes it possible to performthese tasks.

 # GA_PLSmultipleRuns.R  rm(list=ls(all=TRUE)) setwd(″~/Documents/Rdata/CANCEROPOLE/″) # TO BE INFORMED   experidata<− read.csv(″Evolution2D.csv″,sep=″;″,dec=″.″,header=TRUE)   savefile <−″CalEvolution2DcumulVarPLS.txt″   selindiv <−″CalEvolution2D_Xsomes.txt″  #head(data)  library(′MASS′) library(′class′)  library(′GA′)  library(′glm2′)  library(xlsx) library(′pls′)  library(′ChemometricsWithR′) library(′ChemometricsWithRData′) source(″~/Documents/Rpackages/routines/init_pop.R″) source(″~/Documents/Rpackages/routines/pls_fitness_pop.R″) source(″~/Documents/Rpackages/routines/select_pop.R″) source(″~/Documents/Rpackages/routines/crossover_pop.R″)  numcol <−dim(experidata)  maxcol <− numcol[2]  identite <− experidata[,1] VarTarget <− experidata[,2]  #TNR90raw <− experidata[,3]  #TNR30raw <−experidata[,4]  #index30 <− experidata[,5]  #index90 <− experidata[,6] #TNRejaculat <− experidata[,7]  MIR <− experidata[,3:maxcol] # spectrastart at COL3  tutu <− dim(experidata)  spectrumsize10pc <−trunc(0.1*tutu[1])  # Data without the target variable : rawData rawData <− MIR  X <− rawData  XI <− X  XS <− X  # cNames: Names ofvariables  cln <− colnames(X)  n <− length(cln)  cNames <−t(cbind(1:n,colnames(X)))  # creation data.frame with selection of theindex  #plsdata <− data.frame(vary = tnrEjaculat, varx =I(as.matrix(MIR)), row.names = identite)  #attach(plsdata)  # Graphs ofresults PLSR   #(RMSEP(apls), legendpos = ′topright′)   #plot(apls,ncomp=3, asp=1, line=TRUE)   #Sys.sleep(3)  # plot(apls, ncomp = 1, asp= 1, line = TRUE)  # plot(apls, ′loadings′, comps = 1, legendpos =′topleft′, xlab = ′wavenumbers′)  # abline(h = 0)  # Selection stores upto 100 selections (or runs)  selection <− matrix(0,n,1)  # Y : Targetvariable  Y <− TargetVar  # PLOT Average spectrum  frame( )  ymin <−min(XI)  ymax <− max(XI)  meanS <− apply(XI,2, mean)  plot(1:n, meanS,type = ″I″, Iwd = 3, col = ″blue″, ylim = c(ymin, ymax),    xlab =″Discriminant variables″, ylab = ″Average Spectrum″)  #******************************************  number_of_runs = 3 # resetto 100  number_of_passes = 7  max_iterations = 10 # reset to 50 pop_size = 100  for (nrun in 1:number_of_runs)  {   cat(″nrun =″,nrun,″\n″)  for (pass in 1:number_of_passes)  {   cat(″pass :″,pass,″\n″)   pop = list(pop = NULL, size = pop_size, bits = ncol(X),fitness = NULL, P = 0)   pop$P = pop$size   pop$pop = matrix(0, nrow =pop$size, ncol = pop$bits)   pop$pop = init_pop(pop$size, pop$bits)  pop$fitness = pls_fitness_pop(pop$pop, X, Y)   last_idx =length(pop$fitness)   idx = order(pop$fitness)   pop$pop = pop$pop[idx,]  pop$fitness = pop$fitness[idx]   worst_fitness = pop$fitness[last_idx]  for (i in 1:max_iterations)   {    #cat(″ i = ″,i,″\n″)    idx_parents= select_pop(pop$P)    offspring_pop = crossover_pop(pop$pop,idx_parents, pop$bits)    offspring_fitness =pls_fitness_pop(offspring_pop, X, Y)    if (offspring_fitness[1] <worst_fitness)    {     pop$pop[last_idx,] = offspring_pop[1,]    pop$fitness[last_idx] = offspring_fitness[1]     idx =order(pop$fitness)     pop$pop = pop$pop[idx,]     pop$fitness =pop$fitness[idx]     worst_fitness = pop$fitness[last_idx]    }    if(offspring_fitness[2] < worst_fitness)    {     pop$pop[last_idx,] =offspring_pop[2,]     pop$fitness[last_idx] = offspring_fitness[2]    idx = order(pop$fitness)     pop$pop = pop$pop[idx,]     pop$fitness= pop$fitness[idx]     worst_fitness = pop$fitness[last_idx]    }   cat(″Pass ″, pass,″ of ″, number_of_passes,″ iteration ″,i,″ of″,max_iterations,″−−>,″,″ best = ″,pop$fitness[1],″\n″)   #flush.console( )   }   idx = which(pop$pop[1,] != 0)   if (pass== 1)   {    best_ever_variables = cNames[,idx]    best_ever_fitness =pop$fitness[1]   } else {    if (pop$fitness[1] <= best_ever_fitness)   {     best_ever_variables = cNames[,idx]     best_ever_fitness =pop$fitness[1]    }   }   X = X[,idx]   cNames = cNames[,idx]  } best_ever_variables = t(best_ever_variables) colnames(best_ever_variables) = c(″Index″, ″Name″)  best_ever_variables best_ever_fitness  variables <− colnames(MIR)  ind <−rep(0,length(variables))  for (i in 1:length(best_ever_variables[,2])) {  id <− which(best_ever_variables[i,2]==variables)   ind[id] <− 1  } line <− c(best_ever_fitness,trunc(ind))  white <−matrix(0,nrow=n,ncol=1)  # Trace of the var discri on the graph*****************  w <− dim(best_ever_variables)  bw <− w[1]  for (bs in1:bw)  {   sel <− as.double(best_ever_variables[bs,1])  selection[sel,1] <− selection[sel,1]+1.0000   white[sel,1] <− 1  } gfactor <− (ymax−ymin)/max(selection)  plot.new( ) #pdf(filename=″TempHisto.pdf″, 20, 20)  plot(1:n, meanS, type = ″I″,Iwd = 3, col = ″blue″, ylim = c(ymin, ymax),    xlab = ″Discriminantvariables″, ylab = ″Average Spectrum″)  par(bg=NA)  for (bev in 1:n)  {  y2 <− ymin + selection[bev] * gfactor   segments (bev, ymin, bev, y2,col = ″red″)  }  #dev.off( )  # MAJ of the selection fr?quences insavefile (file overwritten upon each run)   cat(″nrun = ″,nrun,″\n″,file= savefile, append=FALSE)   cat(″best fit =″,best_ever_fitness,″\n″,file = savefile, append=TRUE)   for (sf in 1:n)  {   cat(substring(cln[sf],2),″\t″, selection[sf], file = savefile,fill = TRUE, append=TRUE)   }  # MAJ of its individual selections inselindiv  if (nrun==1) {   cat(″Best RMSEP″, substring(cln,2), ″\n″,file = selindiv, fill = FALSE, sep = ″\t″, append = FALSE)   }  cat(line,″\n″, file = selindiv, fill = FALSE, sep = ″\t″, append=TRUE) X <− XS  cln <− colnames(X)  n <− length(cln)  cNames <−t(cbind(1:n,colnames(X)))  }  #savePlot(filename = ″TempHistoPlot″, type= c(″pdf″)

The results are set out in the form of the second derivative spectrum,identification of markers (discriminant variables) and confusion matrix,as is identified in FIG. 1 .

The invention is not limited to the embodiments set out, and otherembodiments will appear clearly for a person skilled in the art.

1-10. (canceled)
 11. A method for determining, in vitro, the risk for anindividual of developing a blood disorder, from a biological sample ofsaid individual, said method comprising the following steps: exposingsaid biological sample to mid-infrared radiation (MIR) of wavelengthvarying from 4000 cm⁻¹ to 400 cm⁻¹ to obtain a spectrum characteristicof said sample, said spectrum being processed in order to obtain aspectral signature made up of absorbance peaks characteristic due totheir position, or wave number, and their intensity, or absorbance, ofthe type and the relative concentrations of the various moleculespresent in said sample; comparing said spectral signature obtained inthe previous step with one or more reference spectral signatures, saidone or more reference spectral signatures being obtained from areference population of individuals; and concluding that: (i) if theintensities of a first group of peaks of the spectral signature of saidindividual is significantly different from the intensities of these samepeaks obtained in the reference spectral signature(s), that theindividual is likely to develop a blood disorder, the first group ofpeaks corresponding to the wave numbers of the following first group:1330 cm⁻¹, 1445 cm⁻¹, 1478 cm⁻¹, 1493 cm⁻¹, 1505 cm⁻¹, 1507 cm⁻¹, 1520cm⁻¹, 1526 cm⁻¹, 1544 cm⁻¹, 1571 cm⁻¹, 1602 cm⁻¹, 1668 cm⁻¹, 1674 cm⁻¹,1676 cm⁻¹, 1697 cm⁻¹, and 2852 cm⁻¹, and (ii) if not, that theindividual is not likely to develop a blood disorder.
 12. The methodaccording to claim 11, wherein, when the individual is likely to developa blood disorder, it is furthermore concluded that: (i) if theintensities of a second group of peaks of the spectral signature of saidindividual is significantly different from the intensities of these samepeaks obtained in the reference spectral signature(s), that theindividual is likely to develop leukemia, the second group of peakscorresponding to the wave numbers of the following first group: 3316cm⁻¹, 3283 cm⁻¹, 3281 cm⁻¹, 3256 cm⁻¹, 3118 cm⁻¹, 3116 cm⁻¹, 1345 cm⁻¹,1343 cm⁻¹, 1340 cm⁻¹ and 1338 cm⁻¹, and (ii) if not, that the individualis likely to develop a myelodysplastic syndrome.
 13. The methodaccording to claim 12, wherein, when the individual is likely to developa myelodysplastic syndrome, it is furthermore concluded that: (i) if theintensities of a third group of peaks of the spectral signature of saidindividual is significantly different from the intensities of these samepeaks obtained in the reference spectral signature(s), that theindividual is likely to develop a low-risk myelodysplastic syndrome, thethird group of peaks corresponding to the wave numbers of the followingfirst group: 3060 cm⁻¹, 3062 cm⁻¹, 3396 cm⁻¹, 3384 cm⁻¹ and 3052 cm⁻¹,and (ii) if not, that the individual is likely to develop a high-riskmyelodysplastic syndrome.
 14. The method according to claim 12, wherein,when the individual is likely to develop leukemia, it is concluded that:(i) if the intensities of a fourth group of peaks of the spectralsignature of said individual is significantly different from theintensities of these same peaks obtained in the reference spectralsignature(s), that the individual is likely to develop a secondaryleukemia, the fourth group of peaks corresponding to the wave numbers ofthe following first group: 3270 cm⁻¹, 3268 cm⁻¹, 3266 cm⁻¹, 3264 cm⁻¹,3192 cm⁻¹, 3190 cm⁻¹, 2850 cm⁻¹, 2840 cm⁻¹, 1707 cm⁻¹, 1705 cm⁻¹, 1664cm⁻¹, 1662 cm⁻¹, 1633 cm⁻¹, 1631 cm⁻¹, 1493 cm⁻¹, 1491 cm⁻¹, 1489 cm⁻¹,1458 cm⁻¹, 1456 cm⁻¹ and 1256 cm⁻¹, and (ii) if not, that the individualwill be likely to develop to develop de novo leukemia.
 15. The methodaccording to claim 11, wherein said biological sample is a blood plasmasample.
 16. The method according to claim 11, wherein the spectrum andthe control spectrum are obtained by the second derivative of infraredspectroscopy data.
 17. A computer program product comprising portions,means or program code instructions for executing the steps of the methodaccording to claim 11 when said program is executed on a computer.